The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial on...
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The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial one. During landing, it is important for the UAV to maintain a constant speed and glide slope to ensure the stability and a successful touchdown on the runway. Also, it is important for a UAV to estimate the accurate point of landing in a minimal amount of time. Embedding bio-inspiring algorithms in UAV control systems helps in accurate estimation of the landing point in a minimal amount of time. In this research work, the bio-inspired optimization algorithms Bats optimization Algorithm, Moth Flame optimization Algorithm and Artificial Bee Colony Algorithm are used in determining the coordinates (points) of the computed path and to determine the optimal point of landing which ensures the above said parameters are within the operational limits of the UAV. The objective of this research work is to determine the path from the computed points and to find the optimal landing point in a minimal amount of time. The difference between the original points of the actual path and the derived computed points of the estimated path is measured as the error rate. The performance of the algorithms is analyzed in terms of two trade-off parameters, the time taken to compute the landing point and the accuracy in predicting the landing point. The empirical results show that the Moth Flame optimization Algorithm takes less time to compute the optimal point with minimal error among the three optimizationalgorithms taken up for the study.
During many past decades it has been known that when using finite word-length binary arithmetic in IIR digital filters, coupled form second order sections implement conjugate-pair poles that have relatively uniform po...
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ISBN:
(纸本)9781728127880
During many past decades it has been known that when using finite word-length binary arithmetic in IIR digital filters, coupled form second order sections implement conjugate-pair poles that have relatively uniform pole locations located inside the unit circle in the z-plane. However, coupled form 2nd order sections could never be used in IIR adaptive filter designs using steepest descent adaptive algorithms due to the multi-modal error surfaces created by coupled form structures. Results presented in this paper demonstrate that the LFFA can be effectively applied to IIR adaptive filter structures designed with parallel or cascade second order coupled form sections.
Metaheuristic algorithms (MH's) are referred to algorithms which has a two-level design 'meta' is upper-level procedure that controls the underlying 'heuristic' which learns and improves a solution...
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ISBN:
(纸本)9781509039890
Metaheuristic algorithms (MH's) are referred to algorithms which has a two-level design 'meta' is upper-level procedure that controls the underlying 'heuristic' which learns and improves a solution iteratively until a sufficiently good solution is obtained for an optimization problem. Since 2008, MH's started to receive attention from researchers around the globe. Variants and new species of MH algorithms emerged. Most of them are claimed to be inspired from the nature or biology. The logics of the search algorithms are mimicked front animal behaviors or nature phenomenon. However, the necessity for creating more new species of such algorithms is doubted. Instead of inventing extra MH's which are similar to one another, we start to ponder if several classical MH's can be used together or in an ensemble. In this paper, the possibilities of putting several MH's into an ensemble are discussed. Different from ensemble in machine learning, we coin this unique collection of Mil's which may fuse together or function cooperatively in solving optimization problems, 'meta-zoo-heuristic'. The term 'zoo' here simply means that the selected MH's are to be kept under control. A preliminary simulation test is conducted, which demonstrates how suitable MH's are selected for a specific problem to solve.
The Permutation Flow Shop scheduling Problem (PFSP) is a typical combinatorial optimization problem. In order to improve the efficacy in solving the PFSP, we applied a discrete mechanism to convert the real value of i...
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ISBN:
(纸本)9781467385015
The Permutation Flow Shop scheduling Problem (PFSP) is a typical combinatorial optimization problem. In order to improve the efficacy in solving the PFSP, we applied a discrete mechanism to convert the real value of individuals into discrete job sequences at first. In particular, a Chaos-based Firefly Algorithm (CFA) is used to optimize the initial population, which provided a superior initial environment and improved the quality of optimization. In addition, the proposed method is tested by seven famous classic benchmark worksheets and compared with Particle Swarm optimization (PSO) and a Genetic Algorithm (GA). At last, the simulation results show that the proposed CFA outperforms the others.
It is well known that cyber criminal gangs are already using advanced and especially intelligent types of Android malware, in order to overcome the out-of-band security measures. This is done in order to broaden and e...
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ISBN:
(纸本)9783319243061;9783319243054
It is well known that cyber criminal gangs are already using advanced and especially intelligent types of Android malware, in order to overcome the out-of-band security measures. This is done in order to broaden and enhance their attacks which mainly target financial and credit foundations and their transactions. It is a fact that most applications used under the Android system are written in Java. The research described herein, proposes the development of an innovative active security system that goes beyond the limits of the existing ones. The developed system acts as an extension on the ART (Android Run Time) Virtual Machine architecture, used by the Android Lolipop 5.0 version. Its main task is the analysis and classification of the Java classes of each application. It is a flexible intelligent system with low requirements in computational resources, named Smart Anti Malware Extension (SAME). It uses the biologically inspiredbiogeography-Based Optimizer (BBO) heuristic algorithm for the training of a Multi-Layer Perceptron (MLP) in order to classify the Java classes of an application as benign or malicious. SAME was run in parallel with the Particle Swarm optimization (PSO), Ant Colony optimization (ACO) and Genetic Algorithm (GA) and it has shown its validity.
This paper analyzes the application of different bio-inspired and heuristic techniques to the problem of curve fitting in experimental chemistry applications. Two different curve models are considered, the well-known ...
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This paper analyzes the application of different bio-inspired and heuristic techniques to the problem of curve fitting in experimental chemistry applications. Two different curve models are considered, the well-known Pearson VII function, used before in different curve fitting applications, and a novel hybrid model for transmittance curves, mainly used in the estimation of nitrates concentration in water samples. We describe the performance of an Evolutionary Programming algorithm, a Particle Swarm optimization technique, a Variable Neighborhood Search algorithm and a Cooperative Coevolution approach in several curve fitting problems, including the processing of real curves such as X-ray diffraction patterns, differential scanning calorimetry curves and transmittance curves of contaminated water samples. (C) 2008 Elsevier B.V. All rights reserved.
This paper proposes an ambitious bio-inspired algorithm for associative classification (AC) based on Quantum-inspired Artificial Immune system (QAIS) for building an efficient classifier by searching association rules...
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ISBN:
(纸本)9781921770142
This paper proposes an ambitious bio-inspired algorithm for associative classification (AC) based on Quantum-inspired Artificial Immune system (QAIS) for building an efficient classifier by searching association rules to find the best subset of rules for all possible association rules. it integrates concepts of quantum computing (QC) and artificial immune system (AIS) as a bio natural inspired algorithm. It employees a mutation operator with a quantum-based rotation gate to control and maintain diversity, and guides the search process. The proposed QAIS is implemented and evaluated using benchmark datasets(Blake & Merz 1998) including Adult, Nursery, Iris and Breast-Cancer datasets. The obtained results are analysed and compared with experimental implementation results of AIS-AC algorithm (Do et al 2009). The experimental results showed that the proposed algorithm is preformed well with large search space and has higher accuracy, and maintained diversity.
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